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Particle filters have revolutionized object tracking in video sequences. The conventional particle filter, also called the CONDENSATION filter, uses the state transition distribution as the proposal distribution, from which the particles are drawn at each iteration. However, the transition distribution does not take into account the current observations, and thus many particles can be wasted in low likelihood regions. One of the most popular methods to improve the performance of particle filters relied on the motion-based proposal density. Although the motivation for motion-based particle filters could be explained on an intuitive level, up until now a mathematical rationale for the improved performance of motion-based particle filters has not been presented. In this letter, we investigate the performance of motion-based particle filters and provide an analytical justification of their superiority over the classical CONDENSATION filter. We rely on the characterization of the optimal proposal density, which minimizes the variance of the particles'weights. However, this density does not admit an analytical expression, making direct sampling from this optimal distribution impossible. We use the Kullback-Leibler (KL) divergence as a similarity measure between density functions and denote a particle filter as superior if the KL divergence between its proposal and the optimal proposal function is lower. We subsequently prove that under mild conditions on the estimated motion vector, the motion-based particle filter outperforms the CONDENSATION filter, in terms of the KL performance measure. Simulation results are presented to support the theoretical analysis.